Product Manager Level 2 - Cincinnati - Ascendum Solutions

    Ascendum Solutions
    Ascendum Solutions Cincinnati

    19 hours ago

    Description

    The Product Manager is responsible for the product planning and execution throughout the Product Lifecycle, including gathering and prioritizing product and customer requirements, defining the product vision, and ensuring revenue and customer satisfaction goals are met. The Product Manager's job also includes ensuring that the product supports the company's overall strategy and goals.

    This role supports an eCommerce fulfillment environment that manages pickup, third-party delivery (Instacart and DoorDash), and operations. The team is building a platform focused on order submission, selection, and routing, with an emphasis on operational reporting, process optimization, and demand forecasting.

    About the Role

    The Product Manager is responsible for the product planning and execution throughout the Product Lifecycle, including gathering and prioritizing product and customer requirements, defining the product vision, and ensuring revenue and customer satisfaction goals are met.

    Responsibilities

    • Manage all technical aspects of product through product lifecycle
    • Work directly and indirectly with business stakeholders, vendors and third parties to ensure execution of deliverables
    • Create, maintain and communicate product catalog and technology roadmaps, including near-term delivery, to engage stakeholders across the organization
    • Identify, measure and improve key product catalog metrics to enhance the customer experience, and create a compelling, relevant product vision using web metrics, customer insights, feedback, research and internal operational metrics
    • Elicit, define and analyze medium to complex requirements in various formats ensuring they are testable, measurable and traceable
    • Set criteria for minimum viable product to increase the speed/frequency with which enhancements and new capabilities are delivered
    • Lead the appropriate teams to refine, prioritize and manage requirements using various tools (e.g., templates, team backlogs, requirements management or agile task management applications)
    • Lead requirement walk-throughs with key stakeholders using various methods (e.g., team demos, workshops, sprint planning and backlog refinement sessions)
    • Identify and estimate anticipated work efforts based on priority using requirement work plans, program increment (PI) planning, and sprint planning
    • Define and resolve dependencies, issues and risks and identify impacted areas through team collaboration
    • Break down a medium to complex vision into smaller projects, initiatives or features

    Qualifications

    Skills: Must-Have

    • Product strategy & prioritization
    • Data platform fundamentals
    • ML literacy
    • Stakeholder communication
    • Designing for expert users without alienating new ones
    • Clear documentation and onboarding flows
    • Understanding user workflows—not just APIs

    Strong Differentiators

    • MLOps understanding
    • Experimentation and metrics fluency
    • Responsible AI leadership
    • Platform UX thinking
    • Stakeholder Management

    Required Skills

    • Align business leaders, engineers, data scientists, legal/compliance, and ops
    • Translate technical constraints into business-relevant language
    • Manage expectations around ML uncertainty and iteration

    Preferred Skills

    • Data Concepts You Should Be Fluent In
    • Data types: structured, semi-structured, unstructured
    • Data pipelines (batch vs. streaming)
    • Data quality dimensions: accuracy, completeness, timeliness
    • Data lineage and observability
    • Metadata, schemas, and versioning
    • Platform Thinking
    • APIs, SDKs, and self-service capabilities
    • Multi-tenant vs. single-tenant design
    • Performance, scalability, and cost tradeoffs
    • Internal vs. external (customer-facing) platforms
    • Machine Learning Fundamentals Every PM Should Know
    • Supervised vs. unsupervised learning
    • Training vs. inference
    • Features, labels, and training data
    • Model evaluation metrics (precision, recall, AUC, RMSE, etc.)
    • Overfitting vs. generalization
    • ML Product Realities
    • ML outputs are probabilistic, not deterministic
    • Model performance degrades over time (data drift, concept drift)
    • Improving models often requires better data, not better algorithms
    • ML development is experimental and iterative
    • Areas that must be understood
    • Model training pipelines
    • Model deployment patterns (batch, real-time, edge)
    • Model monitoring and retraining
    • Versioning of models and data
    • Rollbacks and experimentation (A/B tests, canary releases)
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